Titlebook: Construction Analytics; Forecasting and Inve Mohsen Shahandashti,Bahram Abediniangerabi,Sooin K Textbook 2023 The Editor(s) (if applicable)

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书目名称Construction Analytics影响因子(影响力)




书目名称Construction Analytics影响因子(影响力)学科排名




书目名称Construction Analytics网络公开度




书目名称Construction Analytics网络公开度学科排名




书目名称Construction Analytics被引频次




书目名称Construction Analytics被引频次学科排名




书目名称Construction Analytics年度引用




书目名称Construction Analytics年度引用学科排名




书目名称Construction Analytics读者反馈




书目名称Construction Analytics读者反馈学科排名

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Mohsen Shahandashti,Bahram Abediniangerabi,Sooin KIllustrates theoretical explanations of construction analytics, hands-on practices, and R codes for analytics techniques.Enables readers to investigate the problems in the construction industry such a

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https://doi.org/10.1007/978-3-322-88550-0nhance construction productivity, and reduce construction cost overruns. Although data analytics have tremendous potential to improve strategic decision-making in the construction industry as an ever-increasing volume of data becomes available, it has not been fully exploited on a larger scale in th

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https://doi.org/10.1007/978-3-322-88557-9struction time series data have not shown a constant variance. The volatility of a construction time series variable over time is challenging for accurate forecasting and risk management. This chapter discusses two time series volatility models (i.e., ARCH and GARCH) to forecast the variance of a co

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https://doi.org/10.1007/978-3-322-88557-9ls. This chapter explains the process of identifying the leading indicators of a construction time series and developing proper multivariate models, such as vector error correction and vector autoregressive models for forecasting them. Several practical examples are provided along with R codes to sh

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